Resource allocation for federated learning enabled edge intelligence - Lim, Bryan Wei Yang (Nanyang Technological University, 2022)
The confluence of Edge Computing and Artificial Intelligence (AI) has driven the rise of Edge Intelligence, which leverages the storage, communication, and computation capabilities of end devices and edge servers to empower AI implementation at scale closer to where data is generated. An enabling technology of Edge Intelligence is the privacy-preserving machine learning paradigm known as Federated Learning (FL). In FL, end users carry out model training locally before transmitting the model parameters or gradient updates, rather than the raw data, to a model owner for aggregation. Amid the increasingly stringent privacy regulations, FL has enabled the development of applications that have to be built using sensitive user data and will continue to revolutionize service delivery in the Finance, Internet of Things (IoT), healthcare, and transport industries, among others.